论文标题
超图像分类
Ferrograph image classification
论文作者
论文摘要
识别具有小数据集和各种磨损颗粒尺度的纤维图像的构图很具有挑战性。在本研究中提出了一种新型模型，以应对这些具有挑战性的问题。对于样本不足的问题，我们首先根据图像贴片的排列提出了数据增强算法。然后，提出了图像贴片置换识别的辅助损失函数，以识别数据增强算法生成的图像。此外，我们设计了一个特征提取损失函数，以迫使提出的模型提取更多丰富的特征并减少冗余表示。至于大变化范围磨损粒径的挑战，我们提出了一个多尺度特征提取块，以获得磨损颗粒的多尺度表示。我们在Ferrograph图像数据集和Mini-Cifar-10数据集上进行了实验。实验结果表明，与基线相比，提出的模型可以分别提高两个数据集的准确性9％和20％。
It has been challenging to identify ferrograph images with a small dataset and various scales of wear particle. A novel model is proposed in this study to cope with these challenging problems. For the problem of insufficient samples, we first proposed a data augmentation algorithm based on the permutation of image patches. Then, an auxiliary loss function of image patch permutation recognition was proposed to identify the image generated by the data augmentation algorithm. Moreover, we designed a feature extraction loss function to force the proposed model to extract more abundant features and to reduce redundant representations. As for the challenge of large change range of wear particle size, we proposed a multi-scale feature extraction block to obtain the multi-scale representations of wear particles. We carried out experiments on a ferrograph image dataset and a mini-CIFAR-10 dataset. Experimental results show that the proposed model can improve the accuracy of the two datasets by 9% and 20% respectively compared with the baseline.
